Computer tomography (CT) is a scanning technique that produces an image by measuring the absorption of X-ray energy by a structure of interest. The raw data generated from these measurements can be organized into an absorption map by means of the inverse Radon Transform. The result is the typical radiographic image used in many aspects of research and clinical care. While these images are often subjectively assessed by visual inspection, there is a growing need for more reproducible quantitative measurements within a region of interest. Examples of applications in which such needs exist include:    1. Measurements of airway wall thickness in the lungs. These measurements have been shown to correlate with clinical indices of such lung diseases as Chronic Obstructive Pulmonary Disease (COPD) and are being refined for use as biomarkers for monitoring disease progression.    2. Quantitative assessments of coronary artery narrowing for use as both a diagnostic measure of heart disease and as indices of disease progression.    3. Quantification of lung nodule size. It is estimated that 40% of people who smoke or used to smoke have lung nodules on their CT scans. Factors such as nodule size and rate of change in size are important prognostic pieces of data guiding therapy for possible lung cancer.    4. Quantitative assessments of the amount of cartilage in both normal and injured joints for both diagnostic purposes and therapeutic planning.    5. Assessment of material integrity in quality control processes and flaw detection. CT is sometimes used to detect internal structural problems in materials. The accuracy of the detection process depends in part on the accurate measurement of small defects and cracks.
The challenge when measuring fine structures is that the spatial resolution of the CT scanner imposes a lower limit on the size of a structure that can be accurately assessed. This limit is given by the Nyquist theorem, which states that the ability to quantitatively resolve structure size depends upon the scanner's point spread function (PSF). The scanner PSF is related to the reconstruction process that is done during the inverse Radon Transform. Traditionally, the PSF is modeled as a Gaussian function with a given variance. When the thickness of the structure is of the same order as the variance, the structure measurement is biased towards an overestimation of truth.
Additional factors that influence quantitative structural analysis include the algorithm used to reconstruct the image from the raw data, and the dose of radiation used to acquire the absorption map. Changes in either of these variables between CT scans can make anything more than subjective comparisons inaccurate. These considerations significantly impact longitudinal research based on clinical studies (i.e. following the change in size of a lung nodule when the images were obtained at different hospitals or different scanner settings) and large multicenter studies, where each site may be using a different scanning protocol for image acquisition.